Description of raw data

The objective of this application is to find the differentially expressed genes after using the FAIR_Bioinfo workflow

Conditions

The count data files and associated biological conditions are listed in the following table :

Count table

After loading the data we first have a look at the raw data table itself. The data table contains one row per annotated feature and one column per sequenced sample. Row names of this table are feature IDs (unique identifiers). The table contains raw count values representing the number of reads that map onto the features. For this project, there are 7659 features in the count data table.

Looking at the summary of the count table provides a basic description of these raw counts (min and max values, median, etc).

##         CondA_SRR3105699 CondA_SRR3105698 CondA_SRR3105697
## Min.              0.0000           0.0000           0.0000
## 1st Qu.          60.0000         122.0000          68.0000
## Median          147.0000         313.0000         162.0000
## Mean            353.3178         797.3789         402.3951
## 3rd Qu.         364.0000         791.0000         394.0000
## Max.          21362.0000       58521.0000       21909.0000
##         CondB_SRR3099587 CondB_SRR3099586 CondB_SRR3099585
## Min.              0.0000           0.0000           0.0000
## 1st Qu.          85.0000          72.0000          40.0000
## Median          208.0000         177.0000         127.0000
## Mean            531.7709         471.8327         466.9897
## 3rd Qu.         505.0000         433.0000         370.5000
## Max.          41471.0000       41359.0000       42448.0000

Total read count per sample

Next figure shows the total number of mapped reads for each sample. Reads that map on multiple locations on the transcriptome are counted more than once, as far as they are mapped on less than 50 different loci. We expect total read counts to be similar within conditions, they may be different across conditions. Total counts sometimes vary widely between replicates. This may happen for several reasons, including:

  • different rRNA contamination levels between samples (even between biological replicates);
  • slight differences between library concentrations, since they may be difficult to measure with high precision.;

[…]

Volcano plot

Parameters

##      ColHisto      ColCondA      ColCondB       fitType        pvalue 
##     "#00BFFF"     "#FFD700"     "#4169E1"  "parametric"        "0.05" 
##         logFC pAdjustMethod 
##           "2"          "BH"

R session information

The versions of the R software and Bioconductor packages used for this analysis are listed below. It is important to save them if one wants to re-perform the analysis in the same conditions.

## R version 3.5.1 (2018-07-02)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Debian GNU/Linux 9 (stretch)
## 
## Matrix products: default
## BLAS: /usr/lib/openblas-base/libblas.so.3
## LAPACK: /usr/lib/libopenblasp-r0.2.19.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=C             
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] bindrcpp_0.2.2              shinycssloaders_0.2.0      
##  [3] shinyjs_1.0                 colourpicker_1.0           
##  [5] shinyWidgets_0.4.4          reshape2_1.4.3             
##  [7] plotly_4.8.0                ggplot2_3.1.0              
##  [9] FactoMineR_1.41             DT_0.5                     
## [11] DESeq2_1.22.2               SummarizedExperiment_1.12.0
## [13] DelayedArray_0.8.0          BiocParallel_1.16.5        
## [15] matrixStats_0.54.0          Biobase_2.42.0             
## [17] GenomicRanges_1.34.0        GenomeInfoDb_1.18.1        
## [19] IRanges_2.16.0              S4Vectors_0.20.1           
## [21] BiocGenerics_0.28.0         shinydashboard_0.7.1       
## [23] shiny_1.2.0                
## 
## loaded via a namespace (and not attached):
##  [1] bitops_1.0-6           bit64_0.9-7            RColorBrewer_1.1-2    
##  [4] httr_1.4.0             tools_3.5.1            backports_1.1.3       
##  [7] R6_2.3.0               rpart_4.1-13           Hmisc_4.1-1           
## [10] DBI_1.0.0              lazyeval_0.2.1         colorspace_1.3-2      
## [13] nnet_7.3-12            withr_2.1.2            tidyselect_0.2.5      
## [16] gridExtra_2.3          bit_1.1-14             compiler_3.5.1        
## [19] htmlTable_1.13.1       Cairo_1.5-9            flashClust_1.01-2     
## [22] scales_1.0.0           checkmate_1.9.0        genefilter_1.64.0     
## [25] stringr_1.3.1          digest_0.6.18          foreign_0.8-70        
## [28] rmarkdown_1.11         XVector_0.22.0         base64enc_0.1-3       
## [31] pkgconfig_2.0.2        htmltools_0.3.6        htmlwidgets_1.3       
## [34] rlang_0.3.0.1          rstudioapi_0.8         RSQLite_2.1.1         
## [37] bindr_0.1.1            jsonlite_1.6           crosstalk_1.0.0       
## [40] acepack_1.4.1          dplyr_0.7.8            RCurl_1.95-4.11       
## [43] magrittr_1.5           GenomeInfoDbData_1.2.0 Formula_1.2-3         
## [46] leaps_3.0              Matrix_1.2-14          Rcpp_1.0.0            
## [49] munsell_0.5.0          yaml_2.2.0             scatterplot3d_0.3-41  
## [52] stringi_1.2.4          MASS_7.3-50            zlibbioc_1.28.0       
## [55] plyr_1.8.4             grid_3.5.1             blob_1.1.1            
## [58] promises_1.0.1         crayon_1.3.4           miniUI_0.1.1.1        
## [61] lattice_0.20-35        splines_3.5.1          annotate_1.60.0       
## [64] locfit_1.5-9.1         knitr_1.21             pillar_1.3.1          
## [67] geneplotter_1.60.0     XML_3.98-1.16          glue_1.3.0            
## [70] evaluate_0.12          latticeExtra_0.6-28    data.table_1.11.8     
## [73] httpuv_1.4.5           gtable_0.2.0           purrr_0.2.5           
## [76] tidyr_0.8.2            assertthat_0.2.0       xfun_0.4              
## [79] mime_0.6               xtable_1.8-3           later_0.7.5           
## [82] rsconnect_0.8.12       viridisLite_0.3.0      survival_2.42-3       
## [85] tibble_1.4.2           AnnotationDbi_1.44.0   memoise_1.1.0         
## [88] cluster_2.0.7-1